Quality of Red Wine by TU YEMEI
## [1] "X" "fixed.acidity" "volatile.acidity"
## [4] "citric.acid" "residual.sugar" "chlorides"
## [7] "free.sulfur.dioxide" "total.sulfur.dioxide" "density"
## [10] "pH" "sulphates" "alcohol"
## [13] "quality"## X fixed.acidity volatile.acidity citric.acid
## Min. : 1.0 Min. : 4.60 Min. :0.1200 Min. :0.000
## 1st Qu.: 400.5 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090
## Median : 800.0 Median : 7.90 Median :0.5200 Median :0.260
## Mean : 800.0 Mean : 8.32 Mean :0.5278 Mean :0.271
## 3rd Qu.:1199.5 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420
## Max. :1599.0 Max. :15.90 Max. :1.5800 Max. :1.000
## residual.sugar chlorides free.sulfur.dioxide
## Min. : 0.900 Min. :0.01200 Min. : 1.00
## 1st Qu.: 1.900 1st Qu.:0.07000 1st Qu.: 7.00
## Median : 2.200 Median :0.07900 Median :14.00
## Mean : 2.539 Mean :0.08747 Mean :15.87
## 3rd Qu.: 2.600 3rd Qu.:0.09000 3rd Qu.:21.00
## Max. :15.500 Max. :0.61100 Max. :72.00
## total.sulfur.dioxide density pH sulphates
## Min. : 6.00 Min. :0.9901 Min. :2.740 Min. :0.3300
## 1st Qu.: 22.00 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500
## Median : 38.00 Median :0.9968 Median :3.310 Median :0.6200
## Mean : 46.47 Mean :0.9967 Mean :3.311 Mean :0.6581
## 3rd Qu.: 62.00 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300
## Max. :289.00 Max. :1.0037 Max. :4.010 Max. :2.0000
## alcohol quality
## Min. : 8.40 Min. :3.000
## 1st Qu.: 9.50 1st Qu.:5.000
## Median :10.20 Median :6.000
## Mean :10.42 Mean :5.636
## 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :14.90 Max. :8.000Univariate Plots Section
At first, we can have an overview of the distribution of wine quality:
We can observe that the popular range of acid of red wine
## $x
## [1] "citric acid"
##
## attr(,"class")
## [1] "labels"
I am confused about the distribution, I already made the binwidth to 0.5, but the distribution seems quite weird.
I want to have a view of the other chemical properties distribution
make some adjustment to the chemical properties
## $x
## [1] "chlorides"
##
## attr(,"class")
## [1] "labels"
The free sulfur dioxide and total sulfur dioxide is positively skewed
Univariate Analysis
What is the structure of your dataset?
13varibles,1599 obervations
What is/are the main feature(s) of interest in your dataset?
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
I think three chemical varibles of acid, two varibles of sulfur can be combined as single varible, and with the other chemical properties.
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
some varibles have long tail, and i limit the x scale to the 95% in order to remove some outliers.
Bivariate Plots Section
We can find the top 4factors related to quality is ‘alcohol 0.476’,‘volatile.acidity -0.391’,‘sulphates 0.251, citric.acid 0.226’
I want to add some lines to show the general trend
In this plot, we can observe that alcohol inreases with the quality.
Labeling quality with different levels & boxplot
I want to label quality of wine into several different category, thus I can use box plot to explore it.
Make the same analysis for the other 3 factors and explore the plot
negative relationship between quality and volatile acidity
positive relationship
It is clearly that higher quality has higher content of alcohol.
Since there are six levels, and number of the highest and lowest level are too small to be observed clearly, I plan to combine two levels together, and divide the six levels into three wider levels: Low, Median, High.
In this way, we can get a more clear overview of alcohol distribution and we can apply the same analysis to the other three factors.
Density performs better than histogram in volatile acidity analysis, since from the upper plot, we can only conclude that most wine in the 0.5-xais ,and from the lower plot, higher quality requires lower acidity
Higher quality requires higher citric acid.
This plot is long tale which needs some function, I omit the top 1% of sulphates.
Explore two main variables
We can have a overview of the trends of different variables
Bivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
At first, I calculate the Pearson Correlation of different variables, especilly focus on relationships with quality. And I explore more on top 4 factors influencing quality the most, it turns out that volatile acidity has a negative influence, while citric acid and alcohol have a positive influence on quality.
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
I made a plot to explore chemical properties, which is proved to have weak correlation with quality, and I find that three variables, fixed acidity,free sulfur dioxide and total sulfur dioxide present some peaks.
What was the strongest relationship you found?
Multivariate Plots Section
We already know that alcohol contributes a lot to the quality of wine, and now, I want to insert other variables to see if they contribute to the quality in other way.
From the plot above, takes level-3 for example, it ranges from 0.9925-1.0000, which indicates that density contributes little to quality level, which the alcohol has a weak negative influence on density telling from the plot.
Since the plot is hard to explore, thus I use facet_wrap function to divide it. From the plot, We can infer that low quality between 1-3 lies around 0.5 in y-axis, while quality level of 4-6 tend to have higher y-axis, and also x-axis, thus higher quality wine have higher alcohol and sulphates.
The influence of PH is not obvious
Every quality level has a large range of y-axis, which means residual sugar has little influence on quality.
Lower total sulfur dioxide and higher alcohol produces higher quality.
No obvious correlation
linear analysis:
Predict the wine quality based on chemical properties
##
## Calls:
## m1: lm(formula = quality ~ volatile.acidity, data = wine)
## m2: lm(formula = quality ~ volatile.acidity + alcohol, data = wine)
## m3: lm(formula = quality ~ volatile.acidity + alcohol + sulphates,
## data = wine)
## m4: lm(formula = quality ~ volatile.acidity + alcohol + sulphates +
## citric.acid, data = wine)
## m5: lm(formula = quality ~ volatile.acidity + alcohol + sulphates +
## citric.acid + chlorides, data = wine)
## m6: lm(formula = quality ~ volatile.acidity + alcohol + sulphates +
## citric.acid + chlorides + total.sulfur.dioxide, data = wine)
## m7: lm(formula = quality ~ volatile.acidity + alcohol + sulphates +
## citric.acid + chlorides + total.sulfur.dioxide + density,
## data = wine)
##
## ==========================================================================================================================
## m1 m2 m3 m4 m5 m6 m7
## --------------------------------------------------------------------------------------------------------------------------
## (Intercept) 6.566*** 3.095*** 2.611*** 2.646*** 2.769*** 2.985*** -0.953
## (0.058) (0.184) (0.196) (0.201) (0.202) (0.206) (11.990)
## volatile.acidity -1.761*** -1.384*** -1.221*** -1.265*** -1.155*** -1.104*** -1.114***
## (0.104) (0.095) (0.097) (0.113) (0.115) (0.115) (0.120)
## alcohol 0.314*** 0.309*** 0.309*** 0.292*** 0.276*** 0.280***
## (0.016) (0.016) (0.016) (0.016) (0.017) (0.020)
## sulphates 0.679*** 0.696*** 0.871*** 0.908*** 0.903***
## (0.101) (0.103) (0.111) (0.111) (0.112)
## citric.acid -0.079 0.021 0.065 0.044
## (0.104) (0.106) (0.106) (0.124)
## chlorides -1.663*** -1.763*** -1.747***
## (0.405) (0.403) (0.406)
## total.sulfur.dioxide -0.002*** -0.002***
## (0.001) (0.001)
## density 3.923
## (11.944)
## --------------------------------------------------------------------------------------------------------------------------
## R-squared 0.153 0.317 0.336 0.336 0.343 0.352 0.352
## adj. R-squared 0.152 0.316 0.335 0.334 0.341 0.349 0.349
## sigma 0.744 0.668 0.659 0.659 0.656 0.651 0.652
## F 287.444 370.379 268.912 201.777 166.407 143.910 123.298
## p 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## Log-likelihood -1794.312 -1621.814 -1599.384 -1599.093 -1590.662 -1580.192 -1580.138
## Deviance 883.198 711.796 692.105 691.852 684.595 675.689 675.643
## AIC 3594.624 3251.628 3208.768 3210.186 3195.324 3176.384 3178.276
## BIC 3610.756 3273.136 3235.654 3242.448 3232.964 3219.401 3226.670
## N 1599 1599 1599 1599 1599 1599 1599
## ==========================================================================================================================The model can be described as:wine_quality = 2.985 + 0.276xalcohol - 2.985xvolatile.acidity + 0.908xsulphates + 0.065xcitric.acid - -1.763*chlorides - 0.002xtotal.sulfur.dioxide
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
The density contribute little to quality level, but the alcohol has a negative influence on density. Higher quality wine have higher alcohol and sulphates. The influence of PH is not obvious. Residual sugar has little influence on quality. Lower total sulfur dioxide produces higher quality.
Were there any interesting or surprising interactions between features?
From bivariate plot, we can find some variables, such as residual sugar have peaks with x-axis of quality, but when inserting other variables into analysis, the result show that residual sugar has little influence on quality.
OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
The model can be described as:wine_quality = 2.985 + 0.276xalcohol - 2.985xvolatile.acidity + 0.908xsulphates + 0.065xcitric.acid - -1.763*chlorides - 0.002xtotal.sulfur.dioxide
Final Plots and Summary
Plot One
Description One
This data most lies in quality level of 5-7, and alcohol has an obvious positive influence on quality, the better quality , the higher alcohol percentage. The line is clearyly showed the trend.However, from the linear modeling anlysis, alcohol plays an important role, but only up to 27%, is not the only factor resulting the quality of wine.
Plot Two
Description Two
Plot Three
